code: def train(ctx: Context, params: _Params, model: Model, timer):
      est = ctx.bias
  
      for f in range(model.feature_count):
          start = time.perf_counter()
          trail = (
              model.initial_value
              * model.initial_value
              * (model.feature_count - f - 1)
          )
          fc = _FeatContext(est, f, trail)
          rmse = _train_feature(ctx, params, model, fc)
          end = time.perf_counter()
          _logger.info(
              "[%s] finished feature %d (RMSE=%f) in %.2fs",
              timer,
              f,
              rmse,
              end - start,
          )
  
          est = (
              est
              + model.user_features[ctx.users, f]
              * model.item_features[ctx.items, f]
          )
          est = np.maximum(est, params.rmin)
          est = np.minimum(est, params.rmax)
  
imports: import logging
  import time
  import pandas
  import numpy
  import numba
  from seedbank import numpy_rng
  from .bias import Bias
  from .mf_common import MFPredictor
  from ..None import util
label_desc: Use image node_id 5 for calling a global function with example usage: train(ctx, params, model, timer) without return types